Computation with Memristors

Although memristors have nowadays been showcased as a “non-CMOS device technology”, most research attempts have focused on utilising them in conventional deterministic analogue circuits. One of the first, and probably the only, practical application demonstrated so far is the implementation of memristor-based logic gates. Despite the originality of this work, memristors have been employed as static programmable loads without harnessing their rich internal dynamics. Other analogous examples consist of memristor-based programmable gain amplifiers and adaptive filters, which however so far have only been theoretical in scope. More interesting have been the cases in which the non-linear dynamics of the memristor as well as its “plasticity” are used to resemble the biophysics of the chemical synapse, attracting significant interest within the “neuromorphic” community. Marketing memristors as suitable biomimetic components is based on the conjecture that they possess similar dynamics with chemical synapses and can support Hebbian-type learning rules, such as spike-timing dependant plasticity (STDP). Existing approaches exploit non-volatile elements to do so, ignoring the short-term dynamics, which are however shown to have a major computational role.

Biology reliably exploits dynamical 3-D information by employing membrane dynamics and concentration gradients of various cytoplasmic components, based on probabilistic reaction-diffusion mechanisms. And although it relies on unreliable modules, its computation capacity cannot be matched by modern supercomputers. Recently, we theoretically demonstrated that the outer plexiform layer of the retina can in fact be modelled via complex interconnecting memristive grids to reliably demonstrate biomimetic functionalities, such as image smoothing and edge detection, even in situations where the devices’ yield was as low as 50%. Our ultimate goal is to conceive ultra-low-power dense memristive architectures, embodying many degrees of freedom, for achieving a large interconnectivity and highly parallel processing power capable of solving spatial-temporal problems in real-time.